Tuesday, March 21, 2017

For years, artificial intelligence has been automating tasks—like combing through mountains of legal documents and highlighting keywords—that were once rites of passage for junior attorneys. The bots may soon function as quasi-employees. In the past year, more than 10 major law firms have “hired” Ross, a robotic attorney powered in part by IBM’s Watson artificial intelligence, to perform legal research. Ross is designed to approximate the experience of working with a human lawyer: It can understand questions asked in normal English and provide specific, analytic answers.

Monday, March 20, 2017

Wow, we weren't even three days into the new year before we got the latest in "The machines are taking our jobs!" And I don't just mean in general, I mean specifically lawyers. Every year, we're reminded that our judgment can be mimicked by a computer.

Will software substitute for lawyers, or increase their earning power? There will be evidence of each in coming decades: Routine work will continue to be automated, while new opportunities will also emerge. The critical question is which trend will be dominant, and what its effect will be.

RICHARD SUSSKIND has been discussing “the end of lawyers” for years. He’s at it again, but this time with even more sweeping claims. In a recent book entitled The Future of the Professions, co-authored with his son, Daniel, he argues that nearly all professions are on a path to near-complete automation. Lawyers may be relieved by this new iteration of his argument — if everyone’s profession is doomed to redundancy, then law can’t be a particularly bad career choice after all. To paraphrase Monty Python: few expect the singularity.

Friday, March 17, 2017

In San Francisco, self-driving cars roaming around and people having conversations with Siri have become common sights. Just yesterday, Alexa told me that the meaning of life is 42. As machines become even more woven into our daily lives, we’re witness to a new revolution that's taking over the world.

These methods are often referred to as ‘machine learning’. Rather than trying to encode high-level knowledge and logical reasoning, machine learning employs a bottom-up approach in which algorithms discern relationships by repeating tasks, such as classifying the visual objects in images or transcribing recorded speech into text. Such a system might learn to identify images of cats, for example, by looking at millions of cat photos, or to make a connection between cats and mice based on the way they are referred to throughout large bodies of text.

This method found some early success in simple contrived environments: in ‘SHRDLU’, a virtual world created by the computer scientist Terry Winograd at MIT between 1968-1970, users could talk to the computer in order to move around simple block shapes such as cones and balls. But symbolic logic proved hopelessly inadequate when faced with real-world problems, where fine-tuned symbols broke down in the face of ambiguous definitions and myriad shades of interpretation.

Thursday, March 16, 2017

Big Data — a combination of massive technological power and endlessly detailed voter information — now allows campaigns to pinpoint their most likely supporters. These tools make mobilizing supporters easier, faster and far less expensive than persuading their neighbors.